# 生成示例数据
dt = data.frame(sample = paste0('sample',1:30),
A = runif(30,0,450),
B = runif(30,100,500),
C = runif(30,0,300))
> head(dt)
sample A B C
1 sample1 197.94364 161.0455 223.46878
2 sample2 58.76647 340.5557 164.26801
3 sample3 322.71984 360.9137 88.98755
4 sample4 195.32401 185.9487 213.12908
5 sample5 388.37149 420.9100 40.88598
6 sample6 28.39112 208.1737 236.12682
# 长宽转换
library(tidyverse)
library(reshape2)
dt_long <- melt(dt,
measure.vars = c("A","B",'C'),
variable.name = "group",
value.name = "value")
#measure.vars：度量变量（我们想要放进同一列的变量）
#variable.name：为新列取名，如果不取名，默认新增列的列名就是“variable”
#value.name：新列对应值所在的变量名
head(dt_long)
sample group value
1 sample1 A 197.94364
2 sample2 A 58.76647
3 sample3 A 322.71984
4 sample4 A 195.32401
5 sample5 A 388.37149
6 sample6 A 28.39112
代码解析
runif()R语言中的函数用于创建均匀分布的随机偏差。
用法： runif(n, min, max)
n:表示观察次数
min, max:表示分布的下限和上限
绘图
# 绘图
library(ggplot2)
library(ggthemes)
# 绘制散点图+配对连线
p1 <- ggplot(dt_long,aes(group, value, fill = group))+
geom_line(aes(group = sample),
size = 0.5)+#图层在下，就不会显示到圆心的连线
geom_point(shape = 21,
size = 3,
stroke = 0.6,
color = 'black')+
scale_x_discrete(expand = c(-1.05, 0)) + # 坐标轴起始
scale_fill_manual(values = c('#800040','#fc6666','#108080')) + #设置自己喜欢的颜色
geom_rangeframe() + # 坐标轴分离
theme_tufte() +
theme(legend.position = 'none', # 标签字体等
axis.text.y = element_text(size = 14,
face = "bold"),
axis.text.x = element_text(size =14,
face = "bold"),
axis.title.y = element_text(size = 15,
color = "black",
face = "bold")) +
labs(x = ' ',
y = 'Values')
p1
image.png
# 为了绘制原图的差异形式 手动计算p值,这里用的t检验
library(rstatix)
result = t_test(dt_long,value~group)
head(result)
# A tibble: 3 x 10
.y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
<chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
1 value A B 30 30 -2.16 56.1 0.035 0.07 ns
2 value A C 30 30 1.52 44.7 0.135 0.135 ns
3 value B C 30 30 4.58 49.9 0.0000317 0.0000951 ****
** 添加显著性标记**
p2 <- p1 +
coord_cartesian(clip = 'off',ylim = c(0,500))+
theme(plot.margin = margin(1,0,0,0.5,'cm')) + #设置编剧
annotate('segment',x=1,xend=1.99,y=510,yend=510,color='black',cex=.6)+
annotate('segment',x=1,xend=1,y=505,yend=515,color='black',cex=.6)+
annotate('segment',x=1.99,xend=1.99,y=505,yend=515,color='black',cex=.6)+
annotate('segment',x=2.01,xend=3,y=510,yend=510,color='black',cex=.6)+
annotate('segment',x=2.01,xend=2.01,y=505,yend=515,color='black',cex=.6)+
annotate('segment',x=3,xend=3,y=505,yend=515,color='black',cex=.6)+
annotate('segment',x=1,xend=3,y=530,yend=530,color='black',cex=.6)+
annotate('segment',x=1,xend=1,y=525,yend=535,color='black',cex=.6)+
annotate('segment',x=3,xend=3,y=525,yend=535,color='black',cex=.6)+
annotate("text", x = 1.5, y = 520, label ="p = 0.080",size = 6)+
annotate("text", x = 2.5, y = 520, label ="p = 0.129",size = 6)+
annotate("text", x = 2, y = 540, label ="p = 0.0004",size = 6)
p2
#保存图片
ggsave('paired_scatter.pdf',p2,width = 8,height = 7)
代码解析
coord_cartesian(clip = "off") ，如果加上这一行命令，后面的图置于最上层，不加的效果是显示不全
image.png
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